Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
From Inverse Optimization to Feasibility to ERM
Authors: Saurabh Kumar Mishra, Anant Raj, Sharan Vaswani
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we experimentally validate our approach on synthetic and real-world problems, and demonstrate improved performance compared to existing methods. |
| Researcher Affiliation | Academia | Saurabh Mishra 1 Anant Raj 2 Sharan Vaswani 1 1Simon Fraser University 2SIERRA Project Team (Inria), Coordinated Science Laboratory (CSL), UIUC. |
| Pseudocode | Yes | Algorithm 1 for CILP Input: A, b, Training dataset D (zi, x i )N i=1, Model fθ Initialize θ1 for t = 1, 2, .., T do ˆci = fθt(zi), i [N] for i = 1, 2, .., N do qi = PCi(ˆci) by solving the optimization problem in Eq. (3) end θt+1 = arg minθ 1 2N PN i=1 ||qi fθ(zi)||2 end Output: θT +1 |
| Open Source Code | Yes | 1The code is available here |
| Open Datasets | Yes | We consider two real-world tasks (Vlastelica et al., 2019) Warcraft Shortest Path and Perfect Matching below and defer the synthetic experiments to Appendix C. |
| Dataset Splits | Yes | Both datasets consist of 10000 training samples, 1000 validation samples and 1000 test samples each. |
| Hardware Specification | No | No specific hardware details such as GPU/CPU models, processors, or memory were mentioned for the experimental setup. |
| Software Dependencies | No | The paper mentions using 'CVXPY library (Diamond & Boyd, 2016)', 'ECOS solver (Domahidi et al., 2013)', and 'OSQP solver (Stellato et al., 2020)' but does not specify their version numbers. |
| Experiment Setup | Yes | We train all the methods for 50 epochs with a batch size of 100. We employ a grid search to find the best constant step size in {0.1, 0.05, 0.01, 0.005, 0.001, 0.0005, 0.0001, 0.00005}, across both the Adam and Adagrad optimizers. |